INTRODUCTION Life cycle assessment LCA is a well-recognized tool for evaluating the environmental impacts throughout a product’s life cycle.1From cradle to grave, a product’s life cycle
Trang 1Integrating Hybrid Life Cycle Assessment with Multiobjective
Optimization: A Modeling Framework
Dajun Yue, Shyama Pandya, and Fengqi You *
Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
*S Supporting Information
multiobjective optimization (MOO), the life cycle
optimiza-tion (LCO) framework holds the promise not only to evaluate
the environmental impacts for a given product but also to
compare different alternatives and identify both ecologically
and economically better decisions Despite the recent
methodological developments in LCA, most LCO applications
are developed upon process-based LCA, which results in
system boundary truncation and underestimation of the true
impact In this study, we propose a comprehensive LCO
framework that seamlessly integrates MOO with integrated
hybrid LCA It quantifies both direct and indirect
environ-mental impacts and incorporates them into the decision making process in addition to the more traditional economic criteria The proposed LCO framework is demonstrated through an application on sustainable design of a potential bioethanol supply chain in the UK Results indicate that the proposed hybrid LCO framework identifies a considerable amount of indirect greenhouse gas emissions (up to 58.4%) that are essentially ignored in process-based LCO Among the biomass feedstock options considered, using woody biomass for bioethanol production would be the most preferable choice from a climate perspective, while the mixed use of wheat and wheat straw as feedstocks would be the most cost-effective one
1 INTRODUCTION
Life cycle assessment (LCA) is a well-recognized tool for
evaluating the environmental impacts throughout a product’s
life cycle.1From cradle to grave, a product’s life cycle includes
sourcing of raw materials, logistics, production and use phases,
and end-of-life disposal.2LCA has been widely used to develop
sustainability strategies in both the public and private sectors.3
Many companies and researchers use LCA to compare the
sustainability performances of different alternatives and to
provide guidance in long-term planning and policy making
(e.g., Eco-Efficiency Analysis by BASF).2 , 4
The typical procedure is enumerating all potential alternatives, performing
LCA for each alternative, comparing their indicators of
sustainability (e.g., global warming potential5and Eco-Indicator
996), and then making the choice (e.g., selection of
manufacturing technology and feedstock) according to
designated sustainability criteria.7 However, this approach
becomes intractable when a large or even infinite number of
alternatives are involved.8 Aiming to tackle this challenge, life
cycle optimization (LCO) framework was introduced, and the
importance of a more extensive use of computational
optimization tools in environmentally conscious decision
making was established.9Optimization allows us to incorporate
all potential decisions (e.g., selection of manufacturing
technology, choice of plant location, and capacity of
manufacturing process) into a mathematical model that consists
of multiple objectives (e.g., cost, profit, cumulative energy
consumption, emissions, and water consumption) and constraints (e.g., mass balance, stoichiometry, product speci fi-cation, and resource availability).10−12 Furthermore, the technique significantly facilitates the search for the desired sustainable solution by employing optimization algo-rithms.13−16 Therefore, the combination of LCA with multi-objective optimization (MOO) allows one to conduct a thorough analysis of all potential alternatives and automatically identify both ecologically and economically better decisions within the LCO framework.17,18
Three common LCA methodologies can be potentially employed in an LCO study, namely process-based, input-output (IO)-based, and hybrid LCA.19−21 Most LCO applications in the literature are developed upon the traditional process-based LCA for life cycle inventory (LCI) compila-tion.22The initiative to incorporate LCA objectives in process design and improvement was taken by Fava23and Azapagic.24,25 Following their idea, many researchers have therefore under-taken process-based LCO studies aiming to simultaneously optimize both environmental and economic performances for systems at various scales.26−29 Although process-based LCA provides more accurate and detailed process information, this
Received: September 4, 2015 Revised: December 23, 2015 Accepted: January 11, 2016 Published: January 11, 2016
pubs.acs.org/est
Trang 2“bottom-up” method results in system boundary truncation and
underestimation of the true impact.21Since a large portion of
the life cycle is neglected due to the truncated system
boundary, any decisions made in process-based LCO studies
are based on incomplete life cycle information and may not be
truly optimal.22 In contrast, IO-based LCA is a “top-down”
method that relies on coarse and simplified models derived
from highly aggregated empirical data.30 It utilizes regional/
national economic input-output (EIO) data coupled with
averaged sectoral environmental impact factors.31 While
IO-based LCA has an expanded life cycle boundary, it lacks details
at the process scale Therefore, LCO studies based on IO-based
LCA method are restricted to macroscopic analysis at national
or global levels.32,33Hybrid LCA has been proposed to achieve
a systematically complete LCA system and retain process
specificity.21
It combines the strengths of both process-based
LCA and IO-based LCA and addresses their respective
shortcomings, thus enabling us to quantify both direct and
indirect environmental impacts in a detailed and
comprehen-sive manner.19,20,34
So far, sustainability studies using hybrid LCA focused solely
on analysis with static LCI data Consequently, all processes
and exchanges in the system are predetermined and fixed.22
However, processes to be deployed in practice are influenced
by many factors, for example, availability of feedstock,
acceptance by market, economies of scale, and access to
transportation modes.35Different decisions in various parts of a
system can lead to distinct LCIs In traditional process-based
LCO studies, such changes are captured using fundamental
process and supply chain models, but these benefits do not
automatically extend to hybrid LCO In a hybrid LCO study,
the interactions do not merely exist within the process system
boundary, but also among the numerous industrial sectors in
the economy and between the processes and sectors across the
process system boundary
Very few sustainability studies have attempted to integrate
all-encompassingflexible LCIs with MOO As a pioneer in this
area, Bakshi et al.22,36proposed a“Process-to-Planet” modeling
framework that applies hybrid LCA to sustainable process
design problems from an LCA perspective They developed an
integrated matrix formulation that incorporates models at the
equipment, value chain, and economy scales in a consistent
manner A case study on bioethanol plant design was also
provided Our goal is to develop a general hybrid LCO
modeling framework that allows for thorough analysis of all
alternatives and assists in optimal decision-making based on a
complete life cycle system This work occupies a niche between
the works that combine MOO with process-based LCA and the
works that use single-objective optimization with hybrid LCA
With the aid of mathematical programming methods, the
proposed framework is applied to a supply chain design
problem, which can be closely related to existing works on
process design and process improvement problems.24,36−41We
employ integrated hybrid LCA method for the LCA
component in our LCO framework, which is regarded as the
state of the art in LCA and has a consistent and robust
mathematical framework.42Overview of integrated hybrid LCA
along with detailed mathematical models of the hybrid LCO
framework are presented in the next section We present an
application on sustainable design of a potential bioethanol
supply chain in the UK based on a multiregional input−output
(MRIO) model.43 Decisions such as facility location,
technology selection, production planning, and logistics are
optimized by simultaneously minimizing the total project cost and minimizing the sum of direct and indirect life cycle greenhouse gas (GHG) emissions To the best of our knowledge, this is thefirst time that a hybrid LCO study has been conducted for sustainable supply chain design
2 MATERIALS AND METHODS
2.1 Integrated Hybrid LCA Since its definition by Suh34 and Suh and Huppes,21many researchers have contributed to the development and application of integrated hybrid LCA.42−44 On one hand, integrated hybrid LCA uses process-based LCA methodology to capture key life cycle processes On the other hand, it complements the truncated process system boundary with IO-based LCA methods that include the macroeconomic system within which the processes operate The resulting hybrid LCI, therefore, retains the level of detail and specificity from process-based LCA and has the completeness of an economy-wide system boundary from IO-based LCA.21Exchanges within the process system boundary are represented by a process matrix that describes the inputs of goods to processes in various physical units Exchanges within the economy are represented by the direct requirements matrix that describes interdependencies among various industrial sectors in monetary units at a highly aggregated level.45 The direct requirements matrix can be derived from regional/ national EIO models consisting of a transaction matrix, a value added matrix, and afinal demand vector.46 , 47
Throughout the rest of this article, we will refer to the two systems above as the process system and the IO system, respectively Exchanges across the process and IO systems are captured in upstream and downstream cutoffs matrices.34
Upstream cutoff flows are inputs to the process system that are produced by industrial sectors in the IO system Theseflows are typically specified in monetary units Downstream cutoff flows are outputs from the process system that are consumed by industrial sectors in the
IO system These flows are typically specified in various physical units Market price data are used to convert physical units of flows originating from the process system and monetary units of expenditures originating from the IO system Let m be the number of processes/goods in the process system and n be the number of industrial sectors in the IO system Using a matrix notation, the mathematical basis for integrated hybrid LCA is given by
=
−
−
⎡
⎣
⎢
⎢
⎤
⎦
⎥
⎥
⎡
⎣⎢
⎤
⎦⎥
y
full environmental impact [ ]
0
p io
1
(1)
where Apis a square matrix representing the process inventory with dimension m× m; Aio is the direct requirements matrix with dimension n× n; I is an identity matrix with dimension n
× n; Cuis a matrix representation of upstream cutoffs to the process system with dimension n × m; Cd is a matrix representation of downstream cutoffs to the process system with dimension m × n; ep is the process inventory environ-mental extension vector with dimension 1 × m; eio is the IO environmental extension vector with dimension 1 × n;
y 0]Tis the functional unit column with dimension (m + n)
× 1, where all entries are 0 except for final products from the process system y
Using a single direct requirements matrix for Aiois known as the industry-industry approach This approach does not account for sectors that produce more than one commodity
In addition, supply and use tables (SUTs) can be used to
Trang 3construct Aio, which is known as the commodity-industry
approach that provides greater flexibility in dealing with
multiproduct processes.34,42,43Furthermore, MRIO model can
be used for Aio to represent the global economy Typically, a
two-region model is formulated including the region where our
processes operate and the other region called Rest of the World
(ROW).42,43,48The negative sign assigned to Cuand Cdindicate
the direction of cutoff flows across the process system
boundary Detailed procedures for constructing Cu and Cd
along with techniques to avoid double counting can be found in
the literature.43,44,49−51
For interested readers, we have proposed an illustrative
example that compares the results of process-based LCA and
integrated hybrid LCA The problem is adapted from the classic
toaster example by Suh.34Through the illustrative example, we
demonstrate that ignoring the indirect impacts from the IO
system may cause a considerable underestimation of the true
impacts A hybrid LCA model based on complete life cycle
information is considered more appropriate for decision
making
2.2 Integrated Hybrid LCO Consistent with the structure
of integrated hybrid LCA, the proposed integrated hybrid LCO
model also consists of four parts: process system, IO system,
upstream cutoffs, and downstream cutoffs As illustrated in
Figure 1, the outer layer represents the IO system, and the
inner layer represents the process system The dashed circle
represents the incomplete process system boundary, across
which exchanges between the two systems are depicted
Upstream cutoffs are denoted by the thick arrows originating
from the IO system on the left Downstream cutoffs are
denoted by the thick arrows originating from the process
system on the right Integration of the four parts thus provides
us with a precise and comprehensive framework for decision
making Unlike the static LCI in LCA studies, all four parts of
the proposed integrated hybrid LCO model are flexible
Different decisions made regarding the deployment of
processes can lead to varying hybrid LCIs This relationship
is modeled by “activities” in the process system, which is
defined as a flexible process that involves decision making For
example, a transportation activity can involve the selection of
transportation modes and choice of shipping routes Decisions
made in activities directly influence the process LCI, and indirectly affect exchanges between the process and IO systems and exchanges among different industrial sectors in the IO system Once the decisions in all activities have been made the hybrid LCI would befixed, and the full environmental impact could be measured based on this hybrid LCI The goal of this framework is to use mathematical programming methods to help make optimal decisions in terms of designated sustainability objectives while satisfying all specified constraints This is achieved by combining integrated hybrid LCA with MOO, which allows simultaneous optimization under multiple sustainability objectives In contrast to single-objective optimization, MOO leads to a series of Pareto-optimal solutions rather than a single optimal solution These solutions possess the property that none of their objectives can be unilaterally improved without worsening at least one of the other objectives In this regard, all of these solutions are optimal but emphasize different criteria.52 , 53
A Pareto frontier can be obtained by plotting all Pareto-optimal solutions Solutions on one side of the Pareto frontier are suboptimal, and solutions on the other side are unattainable Therefore, solutions on the frontier indicate the best one can achieve in a sustainable design
or improvement problem It is worth noting that we can set the baseline to zero for design problems since the system is nonexistent before the design is implemented In contrast, the choice of a baseline is critical to the improvement problems, where the alternative solutions must be compared with the original system to evaluate the impact of changes
Instead of building our modeling framework using matrix formulation from an LCA perspective, we devise a general optimization model that seamlessly integrates the process system, IO system, and upstream and downstream cutoffs This model allows us to describe complex systems with mathemat-ical equations and parameters and to represent important decisions with different types of variables As shown below, the environmental and economic objectives are given by (2) and (3); the process system is modeled by (4); the upstream cutoffs are calculated by (5); the IO system is modeled by (6); and the downstream cutoffs are calculated by (7) For clarification, all variables are denoted by upper-case letters and all indices and parameters withfixed values are denoted by lower-case letters
min Obj
m
m p m
n
n io n
env
(2)
∑
= g X Y
l
l l l
con
(3)
∑
Q f X Y m
l
l m, l l
(4)
∑
U n c p Q , n m, In
m
n m m, m
(5)
∑
′
′ ′
P n a P U, n
n
n n n, n
(6)
∑
Q m r m d P, m Out
n
m n n,
(7)
In the above integrated hybrid LCO model, we index the goods/processes in the process system by m and the industrial sectors in the IO system by n The various activities within the process system are indexed by l The environmental objective (2) minimizes the sum of direct and indirect environmental
Figure 1 Illustration of Hybrid LCO framework.
Trang 4impacts from the process and IO systems, where emp and enioare
the environmental impact factors of process m and industrial
sector n, respectively Qmis the total net input/output of good/
process m from all activities in the process system Pn is the
total output of sector n The economic objective (3) minimizes
the total project cost, including the costs from all activities gl(·)
is the cost evaluation function for activity l, which depends on
the value of continuous variables Xl and integer variables Yl
Continuous variables Xl can be used to model decisions on
quantities of raw material acquisition and product sales,
transportation flows, capacity of manufacturing processes,
level of inventory, etc Integer variables Yl can be used to
model decisions on the selection of facility location,
manufacturing technology, capacity level, transportation
mode, etc Constraint (4) indicates that the process LCIs are
dependent on the decisions in all activities, where fl,m(·) stands
for the mapping between process m and the decisions in activity
l Constraint (5) quantifies the upstream cutoffs originating
from the IO system to the process system We use m∈In to
denote the subset of goods m that are inputs to the process
system Unis the total exchange of commodity from sector n to
the process system cn,mis the upstream technical coefficient, of
which detailed derivation procedures are documented by
Wiedmann et al.43 and Acquaye et al.44 pm is the unit price
of good m used to convert physical unitflows into equivalent
expenditures Constraint (6) indicates that the total output of
each sector minus the direct requirements within the economy
should satisfy the requirements by the process system, where
an,n′ is the IO technical coefficients It is worth noting that
Leontief inverse45is not required because the sectoral output is
explicitly denoted by Pn′ and optimization algorithms can
directly handle the linear eq 6 Constraint (7) indicates that
outputs produced from the process system must satisfy the
external demand plus the consumption by various industrial
sectors in the IO system We use m∈Out to denote the subset
of products that are outputs from the process system rmis the
external demand for product m dm,n is the downstream
technical coefficient, of which a detailed derivation can be
found in the works by Suh.34,50
The nature of the resulting optimization problems depends
on a number of factors First, if any of the functions in fl,m(·) or
gl(·) is nonlinear, the resulting optimization problem is a
nonlinear program Second, if any integer variables Yl is
involved, the resulting optimization problem is a mixed-integer
program Third, different applications typically lead to different optimization problems For example, supply chain optimization often leads to mixed-integer programs while process design usually leads to nonlinear programs.13 Solution of different types of mathematical programs requires corresponding optimization algorithms and solvers A number of MOO techniques can be used to simultaneously optimize multiple objectives.54In this work, we choose theε-constraint method for MOO, which is efficient in implementation.55
2.3 Uncertainty Analysis LCA studies are conducted by
an LCA analyst to support the decision maker in making sound choices Therefore, it is critical to improve how uncertainty and variability is communicated in an LCA As suggested by Herrmann et al.,56 the expected uncertainty of an LCA statement (i.e., the answer to an LCA question or inquiry) is dependent on (1) the budget constraints for the LCA analyst, which is decided by the decision maker; (2) the size of the LCA space; and (3) the capability of the analyst Assuming that the budget constraints and the capability of the analyst are constant, Hermann et al.56proposed a taxonomy to scale the expected level of uncertainty in LCAs This taxonomy operates
in six dimensions, and each dimension is read as a switch with two possible settings−either left or right Specifically, the six dimensions and available settings are (1) tangibility−tangible (T) vs intangible (I); (2) repetitivity−single period (S) vs multiple periods (M); (3) scale−micro (i) vs macro (a); (4) time−retrospective (R) vs prospective (P); (5) change− baseline (B) vs change (C); and (6) value−physical (Y) vs value (V) By definition, the left settings corresponds to a lower uncertainty As more dimensions are set to the right position of the switch, the expected inherent uncertainty increases In contrast to most ex-post approaches that occurs after the LCA,57−61 this taxonomy can be applied ex ante to an LCA study We adopt this approach of uncertainty analysis in our hybrid LCO framework Once the goal and scope of a hybrid LCO study is determined, we can classify the study using the taxonomy by Hermann et al.56to help better understand, rank and hence confront uncertainty for LCO
3 RESULTS AND DISCUSSION
3.1 Problem Statement We demonstrate the proposed integrated hybrid LCO framework with an application on sustainable design of a UK advanced biofuel supply chain in this section Faced with increasing concerns on GHG emissions and
Figure 2 Superstructure of the UK advanced biofuel supply chain with the UK map discretized into 34 square cells.
Trang 5energy security, the UK and the wider EU community have
been setting out long-term strategies to promote biofuel
production to substitute traditional fossil fuels.62,63To the best
of our knowledge, existing hybrid LCA studies do not consider
practical factors such as biomass availability, biorefinery
capacity, and geographical variations, whereas existing
sustain-able biofuel supply chain optimization models that do consider
these factors are built exclusively on process-based LCA.8,64,65
We develop a first-of-its-kind hybrid LCO model for biofuel
supply chain applications
The techno-economic supply chain model is adopted from
that by Akgul et al.,66of which the superstructure is given in
Figure 2 The biofuel supply chain consists of three stages,
namely biomass cultivation sites, biorefineries, and demand
centers Biomass feedstocks are acquired from biomass
cultivation sites, where four types of biomass feedstocks are
considered, namely wheat, wheat straw, miscanthus, and woody
biomass (short rotation coppice) We assume that the annual
biomass availability is stable and the soil has been in production
throughout the planning horizon Note that if the land has been
fallow for a long time, tilling to prepare it for cultivation may
release a pulse of CO2.67Biomass feedstocks are converted into
bioethanol using a biochemical route in biorefineries, where a
pretreatment process (ammonia fiber explosion) is employed,
after which lignocellulose can be hydrolyzed and then
fermented We consider four plant capacity levels with different
capital costs Depending on the biomass feedstock used in
biorefineries, the conversion rate and operational cost vary For
the convenience of our data integration procedure, all monetary
values are adjusted to the year of 2011 for inflation and other
relevant financial corrections Conversion to monetary values
representing other years can be performed by using appropriate
inflation indices (e.g., consumer price index) Credits of
coproducts are implicitly accounted for in the parameters on
operational cost Bioethanol produced in biorefineries are sold
to demand centers We adopt one of the demand scenarios in
the paper by Akgul et al.,66in which a total consumption rate of
2802 ton bioethanol/day are distributed to six demand centers
in the UK Three transportation modes are considered for
shipping biomass feedstocks and bioethanol, namely road, rail,
and ship Specifically, the road and rail modes are for intercell
transportation within the UK, the road mode is for local
transportation within a cell, and the ship mode is for
transoceanic transportation of imported biomass from foreign
suppliers As shown in Figure 2, we follow the approach by
Akgul et al.66to discretize the UK map into 34 square cells,
each with dimensions of 108 × 108 km These cells are the
potential locations of biomass cultivation sites, biorefineries,
and demand centers One dummy cell is considered to
represent a foreign wheat supplier All input data on biomass
cultivation, bioethanol production, and transportation are given
in theSupporting Information, which are taken from Akgul et
al.66
A process system and an IO system constitute a complete life
cycle boundary of this problem Theflexible hybrid LCI in this
model is derived based on Ecoinvent v2.268 and the MRIO
model in Wiedmann et al.43A total of 40 relevant processes are
considered in the process system, each corresponding to an
entry in the Ecoinvent database The list of these 40 processes
is presented in the Supporting Information, including
chemicals, biomass, transport services, etc Note that the CO2
sequestration effect during biomass cultivation may vary
depending on whether the soil had been in production previously or fallow
Following the MRIO approach by Wiedmann and his co-workers, the IO system is built based on four tables, namely supply and use tables for the UK, and supply and use tables for the ROW Each table contains 224 sectors/commodities, including mining, grain farming, power generation etc Consequently, the resulting compound IO matrix has a dimension of 896 × 896 (896 = 4 × 224) The structure of the compound matrix was provided in the Supporting Informationof the work by Wiedmann et al.43 Following the approach by Wiedmann et al.,43upstream technical coefficients
in the model are derived by modifying the corresponding IO technical coefficients Sectoral inputs already considered in the process system are nullified to avoid double counting All unit prices for converting physical unit inputs to equivalent expenditures are taken from the original MRIO model.43The downstream cut-offs are neglected as suggested by a number of researchers.44,49−51Six groups of GHGs in the Kyoto Protocol are considered, namely CO2, CH4, N2O, HFCs, PFCs, and
SF6.43 The GWP damage model5 is used to generate an aggregated indicator in the unit of kg CO2 equivalent GHG emissions factors for all processes in the process system are obtained from Ecoinvent v2.2,68 and GWP factors for all industrial sectors in the IO system are obtained from the original MRIO model.43 Note that indirect changes or intangible effects, such as indirect land use changes (ILUC), are not considered in this problem for the simplicity of demonstration However, the proposed hybrid LCO framework
is general that such impacts can be easily incorporated by considering additional parameters, variables, and equations in the optimization model
We consider an environmental objective and an economic objective in this integrated hybrid LCO model The environ-mental objective is to minimize the full life cycle GHG emissions resulting from all supply chain activities, including emissions from both process and IO systems Note that there are other important environmental- and policy-relevant impact indicators than GHG emissions, including acidification, nitrification, resource depletion, respiratory inorganics, land use, human toxicity, etc We consider one environmental objective for the simplicity of demonstration, whereas addi-tional impact indicators can be easily incorporated in the hybrid LCO framework via MOO The economic objective is to minimize the total project cost, including the investment cost, production cost, transportation cost, and import cost This cost
is assumed to be borne by the biofuel manufacturer and does not include externalities The aim is to simultaneously minimize the economic and environmental objectives by optimizing the following decisions:
• Biomass acquisition rate of each type of biomass feedstocks from biomass cultivation sites and foreign suppliers;
• Selection of locations and capacity levels for biorefineries;
• Production rate of bioethanol and consumption rate of biomass at biorefineries;
• Selection of transportation modes and shipping routes for biomass and bioethanol;
• Transportation flows of biomass feedstocks and bioethanol between all facilities
The resulting optimization problem is formulated as a bicriterion mixed-integer linear programming (MILP) problem
A detailed model formulation is provided in the Supporting
Trang 6Information The MILP problems were solved by the solver
CPLEX
According to the taxonomy by Herrmann et al.,56this hybrid
LCO study is classified as ISa-PCY The study is classified as
(1) “intangible (I)” because the actual correlations among
supply, production and demand can be very complicated (e.g.,
competition with petrochemical fuels, legal reasons, CO2
emission taxes) and might not be accurately captured in the
model; (2) “single period (S)” because the annual planning
model considers only a single period; (3)“macro (a)” because
the supply chain under study is at a national scale; (4)
“prospective (P)” because the construction and operation of
this supply chain will take place in the future; (5)“change (C)”
because the development of this supply chain will change the
renewable energy supply; and (6)“physical (Y)” because the
facility locations as well as the quantities of materials, energy
and GHG emissions are specified in the model
3.2 Optimization Results Following the ε-constraint
method for MOO, we have solved 10 cost-minimization
instances with the cap on total life cycle GHG emissions set at
10 different values evenly distributed between its minimum and
maximum values Since this is a design problem, we consider a
baseline with zero life cycle GHG emissions and zero total
project cost As shown by Figure 3, the LCO results indicate
significant trade-offs between the economic and environmental
objectives The stacked column chart shows the breakdowns of
process and IO life cycle GHG emissions of all instances The
line chart shows the total project costs of all instances and
represents the Pareto frontier As the cap on the total life cycle
GHG emissions reduces from 5488 to 2128 ton CO2
-equivalent/day, the total project cost climbs from £ 1.87 to £
2.50 MM/day We observe that the total project cost increases
rapidly as the cap on full life cycle GHG emissions goes below
approximately 2500 ton CO2-equivalent/day All solutions
above the Pareto frontier are suboptimal, and all solutions
below this frontier are unattainable While all solutions on the
Pareto frontier are optimal, solutions on the left emphasize
more on GHG mitigation, and solutions on the right tend to
achieve a more cost-effective supply chain design Specifically,
the point at the upper left corner (Instance 1) has the lowest
full life cycle GHG emissions, so it is considered as the most
preferable solution from a climate perspective; the point at the
lower right corner (Instance 10) has the lowest total project
cost, so it is considered as the most cost-effective solution One
can choose any preferred optimal solution on the Pareto
frontier It is also worth noting that the ratio of process GHG emissions over IO GHG emissions varies over the 10 instances,
as shown by the stacked columns At Instance 1, the process GHG emissions contribute 41.6% of the full life cycle GHG emissions, and the IO GHG emissions contribute 58.4% In contrast, at Instance 10, the process GHG emissions contribute 87.2% of the full life cycle emissions, and the IO GHG emissions contribute 12.8% This difference is due to the
different decisions made in the supply chain system, and we will provide detailed discussions on these two extreme solutions in the following paragraphs
Results of Instance 1 are obtained by minimizing the full life cycle GHG emissions without setting any budget on the project cost The corresponding supply chain configuration is given in
Figure 4a A total of six biorefineries are built in the same cells
as the locations of demand centers Two biorefineries are at capacity level 1, three at capacity level 3, and one at capacity level 4 The daily demand of 2802 tons of bioethanol is exclusively produced from 11 924 tons of woody biomass This result indicates that producing bioethanol from woody biomass
is the option that leads to the fewest life cycle GHG emissions All woody biomass is acquired locally in the same cells as the locations of biorefineries in order to avoid long-distance
Figure 3 Pareto profile with the total project cost and life cycle GHG emissions for 10 instances.
Figure 4 Optimal supply chain con figuration and material flows between the facilities for (a) the solution with minimum full life cycle GHG emissions, and (b) the solution with minimum total project cost.
Trang 7biomass transportation Rail is the preferred mode of
transportation for shipping bioethanol from biorefineries to
demand centers because of the lower unit transportation GHG
footprint compared to road transport The total project cost of
this solution levelized on a daily basis is £ 2.50 MM/day The
major cost comes from production, which accounts for 77% of
the total project cost; the investment cost levelized on a daily
basis accounts for 16%; and the sum of local and inter-region
transportation costs collectively accounts for 7% The sum of
direct and indirect GHG emissions of this solution is 2128 ton
CO2-equivalent/day Life cycle GHG emissions profiles are
given in theSupporting Information, where we summarize the
GHG emissions from each process in the process system, and
each industrial sector in the UK IO system and ROW IO
system In the process system, the major contributor is the
production of ammonia, which results in 387 ton CO2
-equivalent/day The conversion of woody biomass to
bioethanol requires significant use of ammonia during biomass
pretreatment, and the ammonia manufacturing process is
energy-intensive The second largest contributor in the process
system is from acquisition of the biomass feedstock−woody
biomass, which results in 133 ton CO2-equivalent/day These
GHG emissions are primarily due to chemical and energy use in
the cultivation, collection and preparation phases of the woody
biomass feedstock In the IO system, the major contributor is
the sector of natural gas and services in the ROW, which results
in 205 ton CO2-equivalent/day, reflecting the fact that
imported natural gas plays a significant role in the UK’s energy
supply The second largest contributor in the IO system is the
sector of electricity production from coal in the UK, which
results in 147 ton CO2-equivalent/day, reflecting the fact that
majority of the power supply in the UK comes from coal-fired
power plants
On the opposite side of the solution above, results of
Instance 10 are obtained by minimizing the total project cost
without setting any cap on the life cycle GHG emissions The
corresponding supply chain design is shown in Figure 4b A
total of four biorefineries are built, and all biorefineries are at
capacity level 4 in order to take advantage of economies of
scale The daily demand of 2802 tons of bioethanol is produced
from 5701 tons of wheat and 3589 tons of wheat straw In this
solution, wheat is chosen as the major biomass feedstock as it
has the lowest unit production cost compared to other types of
biomass feedstocks Since the availability of domestic wheat
does not suffice the total requirement for bioethanol
production, wheat straw is chosen as the secondary biomass
feedstock Wheat straw is a coproduct of wheat acquisition, thus
having a lower acquisition cost than miscanthus and woody
biomass As shown in Figure 4b, three of the biorefineries
source their biomass feedstocks from local biomass cultivation
sites The only exception is the biorefinery in cell 7, which
obtains most biomass feedstocks from biomass cultivation sites
in the surrounding cells Rail is the preferred transportation
mode for shipping both biomass and bioethanol, because it has
a lower unit transportation cost compared to road transport
The total project cost of this solution levelized on a daily basis
is £ 1.87 MM/day The production cost accounts for 70% of
the total project cost; the investment cost accounts for 18%;
and the transportation cost accounts for 12% The ratio of
investment cost over production cost is increased compared to
Instance 1, because of the deployment of larger-size
biorefineries that benefit from economies of scale The
transportation cost is 5% higher than that at Instance 1 due
to the long-distance transportation of wheat and wheat straw at Instance 10 The full life cycle GHG emissions of this solution
is 5488 ton CO2-equivalent/day, which is more than twice of that of Instance 1 Life cycle GHG emissions profiles are given
in theSupporting Information In the process system, the major contributor is acquisition of wheat, which results in 2929 ton
CO2-equivalent/day The second largest contributor is acquisition of wheat straw, which results in 728 ton CO2 -equivalent/day Both biomass feedstocks require significant use
of energy, water, and fertilizers during the cultivation and acquisition phase In the IO system, the major contributor is the sector of electricity production from coal in the UK, which results in 145 ton CO2-equivalent/day, and the second largest contributor is the sector of natural gas and services in the ROW, which results in 94 ton CO2-equivalent/day This distribution of emissions is similar to that of the previous solution, reflecting that natural gas and power are the major sources of GHG emissions
3.3 Comparison with Other Studies We briefly review the hybrid LCA results on bioethanol production from other studies in this section To facilitate the comparison, we convert all life cycle GHG emissions data to a unit of energy, that is, GJ bioethanol The specific energy of bioethanol is assumed to be 23.4 MJ/kg and the energy density is 18.4 MJ/L.69According
to these assumptions, the lowest life cycle GHG emissions in our LCO results is 32.45 kg CO2equivalent/GJ at Instance 1, and the highest life cycle GHG emissions is 83.69 kg CO2 equivalent/GJ at Instance 10
Bright et al.70 undertook an environmental assessment of wood-based biofuel production to evaluate the GHG mitigation potentials under different consumption scenarios in Norway using a hybrid biregion LCA method The reported full life cycle GHG emissions are 21 and 27 kg CO2equivalent/GJ for bioethanol production via thermochemical conversion and biochemical conversion, respectively
Acquaye et al.42calculated the life cycle GHG emissions of biodiesel and bioethanol produced from various biomass feedstocks in UK using a hybrid MRIO LCA framework The reported sum of process and IO GHG emissions are 25.1, 29.1, and 72.9 kg CO2equivalent/GJ for bioethanol produced from sugar cane, sugar beet, and corn, respectively Specifically, the
IO GHG emissions account for 36.7%, 8.6%, and 3.6% of the full life cycle GHG emissions for bioethanol produced sugar cane, sugar beet, and corn, respectively
Palma-Rojas et al.71evaluated the energy use, life cycle GHG emissions, and employment impact of bioethanol production from bagasse in Brazil using the integrated hybrid LCA method The reported sum of process and IO GHG emissions is 60.0 kg
CO2equivalent/GJ, where the IO GHG emissions account for 49.3% of the full life cycle GHG emissions
In summary, the full life cycle GHG emissions values obtained from our hybrid LCO study are on the same order of magnitude as the literature values reviewed above Differences
in values are due to the choice of data set and biomass feedstocks Consistent with our hybrid LCO results, these literature values also suggest that the unit life cycle emissions as well as the ratio of process emissions over IO emissions vary significantly by the biomass feedstocks used for bioethanol production
3.4 Limitations A limitation of the proposed hybrid LCO framework is that it has been tested on only a limited number
of case studies More examples are needed to demonstrate the applicability of the proposed framework Another limitation lies
Trang 8in that hybrid LCO study requires much more efforts in data
collection and data processing compared to traditional
process-based LCO The policy relevance of the optimization results is
dependent on the quality of the data used
*S Supporting Information
The Supporting Information is available free of charge on the
ACS Publications websiteat DOI:10.1021/acs.est.5b04279
Detailed mathematical model formulation, notations and
input/output data for the case study (PDF)
(XLSX)
Corresponding Author
*Phone: (847) 467-2943; fax: (847) 491-3728; e-mail:you@
northwestern.edu
Notes
The authors declare no competingfinancial interest
We greatly thank Dr Thomas O Wiedmann at the University
of New South Wales in Australia and Dr Lazaros Papageorgiou
at University College London in the United Kingdom for use of
their data and models and for their guidance and helpful
discussion The paper has been greatly improved by the
insightful and constructive feedback from the associate editor
and three anonymous reviewers We acknowledge partial
financial support from the National Science Foundation
(NSF) CAREER Award (CBET-1554424)
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